Suitability and managerial implications of a Master Surgical Scheduling approach

Abstract: Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tactical OR planning approaches often fail to account for these issues.Master surgical scheduling (MSS) is a promising approach for hospitals to optimize resource utilization and patient flows. We discuss the pros and cons of MSS and compare MSS with centralized and decentralized planning approaches. Finally, we address various implementation issues of MSS and discuss its suitability for hospitals with different organizational foci and culture

Efficient Process Model Discovery Using Maximal Pattern Mining

In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques

On convex feasibility problems

In this paper we consider a projection method for convex feasibility problem that is known to converge only weakly. Exploiting a property concerning the intersection of a family of convex closed sets, we present a condition that makes them strongly convergent, without additional assumptions. AMS Subject Classification: Primary: (K5J15, 47N10, secondary 41a29, 47H05

Which kind of knowledge is suitable for redesigning hospital logistic processes?

A knowledge management perspective is rarely used to model a process. Using the cognitive perspective on knowledge management in which we start our analysis with events and knowledge (bottom-up) instead of with processes and units (top-down), we propose a new approach for redesigning hospital logistic processes. To increase the care efficiency of multi-disciplinary patients, tailored knowledge in content and type that supports the reorganization of care should be provided. We discuss the advantages of several techniques in providing robust knowledge about the logistic hospital process by employing electronic patient records (EPR's) and diagnosis treatment combinations (DTC's)

Process Mining: Discovering Direct Successors in Process Logs

Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method